Graph selection with GGMselect
Christophe Giraud (MIAJ, CMAP), Sylvie Huet (MIAJ), Nicolas Verzelen, (LM-Orsay)

TL;DR
This paper introduces a two-stage graph estimation method for high-dimensional Gaussian graphical models, demonstrating its consistency, risk control, and practical effectiveness on biological gene expression data.
Contribution
It proposes a novel two-stage graph selection procedure with theoretical guarantees and practical implementation for high-dimensional Gaussian graphical models.
Findings
Method is consistent in high-dimensional settings
Risk is controlled by a non-asymptotic oracle inequality
Effective on real gene expression data
Abstract
Applications on inference of biological networks have raised a strong interest in the problem of graph estimation in high-dimensional Gaussian graphical models. To handle this problem, we propose a two-stage procedure which first builds a family of candidate graphs from the data, and then selects one graph among this family according to a dedicated criterion. This estimation procedure is shown to be consistent in a high-dimensional setting, and its risk is controlled by a non-asymptotic oracle-like inequality. The procedure is tested on a real data set concerning gene expression data, and its performances are assessed on the basis of a large numerical study. The procedure is implemented in the R-package GGMselect available on the CRAN.
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Taxonomy
TopicsGene expression and cancer classification · Statistical Methods and Inference · Bayesian Modeling and Causal Inference
